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Event‐Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled‐Data Information

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  • Hongjie Li

Abstract

The paper investigates the state estimation problem for a class of recurrent neural networks with sampled‐data information and time‐varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event‐triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed‐input approach, the error dynamic system is equivalent to a dynamic system with two different time‐varying delays. Based on the Lyapunov‐krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event‐triggered scheme.

Suggested Citation

  • Hongjie Li, 2012. "Event‐Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled‐Data Information," Abstract and Applied Analysis, John Wiley & Sons, vol. 2012(1).
    • Handle: RePEc:wly:jnlaaa:v:2012:y:2012:i:1:n:731453
    DOI: 10.1155/2012/731453
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    References listed on IDEAS

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    1. Park, Ju H., 2008. "On global stability criterion of neural networks with continuously distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 37(2), pages 444-449.
    2. Park, Ju H. & Kwon, O.M., 2009. "Global stability for neural networks of neutral-type with interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1174-1181.
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